Sandeep Singh

Sandeep Singh

Expert Senior Director

Bain & Company

Sandeep Singh is a passionate leader and expert in Deep Learning and Artificial Intelligence. With a strong background in computer science and extensive experience in machine learning research, he has made significant contributions to the advancement of AI technology. 

Sandeep has delivered numerous talks and workshops worldwide, inspiring and educating audiences about AI's potential to revolutionize various industries. As a dedicated mentor, he actively guides aspiring AI professionals and fosters innovation in the field. Currently serving as Expert Senior Director at Bain & Company, Sandeep continues to drive innovation and push the boundaries of AI technology.

In this full-day hands-on masterclass, participants will learn how to leverage AI-powered development platforms to build production-ready software at unprecedented speed. You’ll master six industry-leading tools like Claude Code, OpenAI Codex, Cursor, Replit, V0, and Google Gemini and discover how to orchestrate them for maximum enterprise impact. 

We will cover AI development methodologies including SpecKit, OpenSpec, and Claude Code PM, explore extensibility through MCP servers and Sub-Agents, and dive into practical implementations across web applications, mobile apps, and microservices. By the end, attendees will understand how to design, document, and deliver software across the entire lifecycle from PRDs to deployment using AI-native workflows. 

Prerequisites 

  • Familiarity with software development concepts and the software lifecycle 
  • Basic understanding of enterprise software roles (developer, PM, QA, architect) 
  • No programming or coding experience required 
  • Laptop with internet access and ability to create accounts on cloud platforms 
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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

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